{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "aa1fcc23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt  # 导入pyplot模块\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 解决中文乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决正负号乱码问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6325085c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fields1 = []\n",
    "data1 = []\n",
    "fields2 = []\n",
    "data2 = []\n",
    "failure_count_of_model = [0,0,0,0,0,0]\n",
    "all_count_of_model = [0,0,0,0,0,0]\n",
    "failure_percen_list = [0.0,0.0,0.0,0.0,0.0,0.0]\n",
    "fnum = 0\n",
    "anum = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "08fb6811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18387\n",
      "706182\n"
     ]
    }
   ],
   "source": [
    "f1 = open('./data/ssd_failure_tag.csv','r')\n",
    "failure = csv.reader(f1,delimiter=',')\n",
    "fields1 = failure.__next__()\n",
    "for row in failure:\n",
    "    data1.append(row)\n",
    "#    print(row)\n",
    "    fnum = fnum + 1\n",
    "print(fnum)\n",
    "\n",
    "f2 = open('./data/20191231.csv','r')\n",
    "day_data = csv.reader(f2,delimiter=',')\n",
    "fields2 = day_data.__next__()\n",
    "for row in day_data:\n",
    "    data2.append(row)\n",
    "#    print(row)\n",
    "    anum = anum + 1\n",
    "print(anum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4d2940a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19, 28, 116, 276, 244, 64]\n",
      "[46, 255, 5937, 25860, 100214, 65095]\n",
      "197407\n",
      "[0.009624785341958491, 0.014183894188149356, 0.05876184735090447, 0.1398126712831865, 0.1236025064967301, 0.03242032957291281]\n",
      "0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'A1':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'A1':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of A1(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4,5,6]# 斯皮尔曼相关系数\n",
    "x2 = [1,2,4,6,5,3]\n",
    "d = [0,0,0,0,0,0]\n",
    "dd = [0,0,0,0,0,0]\n",
    "for i in range(0,6,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,6,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-6*sum_dd/(6*(6*6-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b8cc6238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 9, 122, 278, 448, 11]\n",
      "[13, 81, 393, 6702, 35483, 12144]\n",
      "54816\n",
      "[0.018242848803269117, 0.016418563922942206, 0.22256275539988324, 0.5071511967308815, 0.8172796263864566, 0.02006713368359603]\n",
      "0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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G5sbc3CNNkiRJUp+67gF5J80f9OsAR4ydrKpTgSfSrGD15gHK/x6wKMnaYyfalax2AM6pql4T1Cfz2/b44B5pj26Pk61uJUmSJKkPXQcg6wDfqaprgBqfUFW30uzXse4A5R/Ylnt4ktWTLAscTLP61ZdnUlBV/Qk4Ddgtyb+CkCT3Av6rffrNAdoqSZIkaYKul+G9Erj/FOlrc8fQpxmrqjOS7EGzc/nFwC3AMjSBxH5j+ZLsA1xVVR9dTJFvAP4HODPJ0cCNwLY080wOrqpf99tWSZIkSXfVdQ/Id4HnJdl8YkKS5wHPbPP0rao+AWwOfIsm8HgPsFlVjZ+vsSdNcLG4sk6jmT9yNLAlzapXt9AMJVvs9ZIkSZJmpusekHcD2wAn0G4I2PYsrAM8AvgzTcAwkKo6BThlivRMltYj7x+Alw/aJkmSJEmL12kPSFX9jaZH4TCa4CbAs4EHtuc2rqqZrFQlSZIkaR7pugdkLAjZNckraDb6A7i8qmqKyyRJkiQtAJ0HIGPagONvs1W+JEmSpNHT+U7ok0myVJI9kzx3WHVKkiRJWrJ0vRP6H5P03Giwqm4D/g3Yt8s6JUmSJI2OrntA1gDuN0X67+i987gkSZKkBWBoQ7BaKwOXD7lOSZIkSUuIgSehJ9lrwqnNe5wDWBN4EXDIoHVKkiRJGk1drIL1vgnPN28fE90KfJ1ml3FJkiRJC1AXAcia7THAH4FPAvtPyHM7cFlV3dpBfZIkSZJG1MABSFVdNPZzEoBrxp+TJEmSpDFdT0K/CLiq4zIlSZIkzROd7oReVWsuPpckSZKkhWrYy/CS5GHDrlOSJEnSkqHTHhCAJE8GdqGZnL7UhOTlgUcBy3ZdryRJkqQlX6cBSJJnAUcBN9P0riwF/Jkm4FgF+AdwSpd1SpIkSRodXQ/BejdwHrA68DKapXk3AVYDDqAJSNwHRJIkSVqgug5A1gW+X1VXAd8BClhUVQXsQbNK1vs7rlOSJEnSiOg6APkncC+AqroBOBd4Qvv8duAHwL91XKckSZKkEdF1AHIa8PwkT2+f/wTYLslYPQ8Cbum4TkmSJEkjYjbmgAT4dvv8KzTDsn6X5CRgB+D4juuUJEmSNCI6DUCq6kxgY+CT7fOTgbcDDwU2A34NvKXLOiVJkiSNjs43IqyqP1TVHuOefwS4D3D/qnpCVf256zolSZIkjYah7IReVbe3K2ORZP1h1ClJkiRpyTNQAJLkCUmOTXJFkhuSnJXkbUnussFhkqckORY4c5A6JUmSJI2uvndCT/IY4CRgOZqVra4BHgl8GHg0sEubb0fgbcBGNBPUfzZYkyVJkiSNqkF6QN5DE3y8C7hXVa1Ms8zuj4EXJdk7ybnAN2j2Ajka2LSqNh2syZIkSZJGVd89IMDmwElV9eGxE1V1aZLXAb+lCUxuBA4G9q+qCwZqqSRJIySvOn2um9C3OmTDuW6CpHlskADkATTL6k50dnv8DvCKqrpigDokSZIkzSODDMEKcP3Ek1VV7Y9nGnxIkiRJGm+QHhCADZLsNtO0qvrMgPVKkiRJGkGDBiBPbx+9bNcjLUABBiCSJEnSAjRIAPL+zlohSZIkaUHoOwCpKgMQSZIkSTMy0E7okiRJkjQTBiCSJEmShsYARJIkSdLQGIBIkiRJGhoDEEmSJElDYwAiSZIkaWgMQCRJkiQNjQGIJEmSpKExAJEkSZI0NAYgkiRJkobGAESSJEnS0HQegCRZPsnm457fP8l7knwiyRZd1ydJkiRpdNy9y8KSPBI4DrgFeFiSewOnAw9ps7w2yTOq6odd1itJkiRpNHTdA/IhYDlg9/b5C2mCj9cDWwBXAO/suE5JkiRJI6LTHhBgE+DrVfWd9vlTgN9U1UEASb4JvKjjOiVJkiSNiK57QJYFbhr3fGPgp+Oe3wRUx3VKkiRJGhFdByDnAs9KskqSZwEPA34BkCTA5sD5HdcpSZIkaUR0HYDsD6wFXAocTTPn46gkawCnARsAn+24TkmSJEkjotM5IFX11aajg52BvwH7VdW1SdammYz+4aoyAJEkSZIWqK4noVNVXwW+OuHcr4BVuq5LkiRJ0mhxJ3RJkiRJQzMrAUiSNZO8JMnb2+cbJ3lWOxFdkiRJ0gLVaQCSZKkkXwDOAw6l2ZgQYCPg28D3kizbZZ2SJEmSRkfXPSB7AS8D/rd9jPk28CngacDbO65TkiRJ0ojoOgB5KfCTqtqWZtldAKrqz1X1RuAHuBO6JEmStGB1HYCsCpw6Rfo5wIM7rlOSJEnSiOg6ALkIWG+K9E2BP3dcpyRJkqQR0XUA8iVg2yTvAe49djLJKkkOAJ4A/HfHdUqSJEkaEV1vRPhhYBPg/UABJLkauA8Q4IdtHkmSJEkLUKc9IFV1K/BMmpWwjgX+AFza/vwyYLs2z0CS3C/JQUkuSXJdkkOTLD9ouePKPyHJiV2VJ0mSJKnRdQ8IVVU0w6xmZahVkuVoelI2ao/nAi8GHkizzO+g5b8cWAScNGhZkiRJku5sVnZCn2Vvpgk+9q+qbdvlfbcFtkmy8yAFJ3kAsF8HbZQkSZLUwygGIK8ErqfZ9BCAqjoNOB544YBlfxS4L3DZgOVIkiRJ6qHTACTJn5Ic1WWZE8pfFVgTOL6qbpiQfDzN0Kl+y34qsAvwMZphXZIkSZI61vUckL8BD+q4zPFWaY9n9ki7AFghyYpVdeVMCk2yDHAw8EeanpXvz/D6syZJWmsm5UiSJEnzXddDsD4LPC7JYzoud8zYSleX90i7pj2u1ke5ewIPB3arqn/20zBJkiRJi9dpD0hVfTbJlsB3k+xYVb/osnxgbAnff/RIu7k93nMmBSZZF/hP4ItV9aN+GlVV609S9llMvTO8JEmStKB0GoAk2Qs4G9gY+Fk7H+S3E7JVVe3dZxVj8z7u0SNt6SnSekoS4BDgKmCPPtskSZIkaZq6ngPyvgnPd2wf4xXQbwByaXtco0fayu3x6hmUtyuwObBzVV3VZ5skSZIkTVPXAciaHZd3J1X19ySXABv0SN64Pf51BkXuS9P78dgkjx13fk2AJPsAF1bV5/tpryRJkqQ763oOyEVdljeJ7wG7Jlm7qs6Hf61itQNwTlX1mqA+mRXb456TpO9JsyO6AYgkSZLUgVHciPBAmmFchydZPcmyNEvorgZ8eSYFVVV6PWiCjpPa54u6/gUkSZKkhWo2JqEvziCT0KmqM5LsQbNh4MXALcAywGnAfuPasg9wVVV9tN+6JEmSJHVrtiehj1dAGGwSelNQ1SeSnA7sDqwKHAfsW1U3j8u2J3ARYAAiSZIkLSFmexL6UsBDgKfQBAs/BV7fRUVVdQpwyhTpGaDsRf1eK0mSJGlyw5iE/kfgxCQHA8cD2wMf77JeSZIkSaNhaJPQq+qvNDuOv3ZYdUqSJElasgx7FawbgdWHXKckSZKkJUTXq2A9ZIp6Hgl8hGZIliRJkqQFqOtJ6H+iWeVqMrcAO3VcpyRJkqQR0XUA8gF6ByAFXA78oKou7LhOSZIkSSOi61Ww3tdleZIkSZLml2FPQpckSZK0gBmASJIkSRoaAxBJkiRJQ2MAIkmSJGlo+p6EnqTf/Tyqqtbqt15JkiRJo2uQVbAuZuo9PyRJkiTpTvoOQKpqUYftkCRJkrQAOAdEkiRJ0tAYgEiSJEkaGgMQSZIkSUNjACJJkiRpaAxAJEmSJA2NAYgkSZKkoTEAkSRJkjQ0g+yEvnn742lVdeOEc1OqqpP7rVeSJEnS6BpkJ/QTaXZCfyRw7oRzk0mbvtQA9UqSJEkaUYMEILvSBBOXjTv38sGaI0mSJGk+6zsAqapDe5w7bKDWSJIkSZrXnIQuSZIkaWgMQCRJkiQNjQGIJEmSpKEZegCSZIVh1ylJkiRpyTDIKlg9JVkLeD6wJnddbnd5YHNg5a7rlSRJkrTk6zQASbIpcBywXHuqaPb+GPsZ4E9d1ilJkiRpdHQ9BGsf4EpgPeCNwG3A/YH7AEcA1wBP77hOSZIkSSOi6wDkMcBRVXUO8GWaIViLquoGYBfgBpogRZIkSdIC1HUAUv/6oepq4I/AE9rnNwHHAFt1XKckSZKkEdF1AHIGsFOS9drnpwDbjEu/1yzUKUmSJGlEdB0MfIBmzsev2uffAjZM8sMkhwEvBH7WcZ2SJEmSRkSnq2BV1UlJtgJ2b59/J8lBwGvbLJcCb+2yTkmSJEmjo/PhUFV1clXtOO7564EHARsCa1XVWV3XKUmSJGk0dL4RYS9VdSlN74ckSZKkBazvHpAkVyZ5R5eNkSRJkjS/DTIE677APXslJHl2knUGKFuSJEnSPDRbS+IeDbxolsqWJEmSNKLck0OSJEnS0BiASJIkSRoaAxBJkiRJQzPoMrwPTbL5TNOq6uQB65UkSZI0ggYNQF7cPmaattSA9UqSJEkaQYMEIId11gpJkiRJC0LfAUhVvbzLhkiSJEma/5yELkmSJGloDEAkSZIkDY0BiCRJkqShMQCRJEmSNDQGIJIkSZKGxgBEkiRJ0tAYgEiSJEkaGgMQSZIkSUNjACJJkiRpaAxAJEmSJA2NAYgkSZKkobn7bBSaZBGwGbBqVb0uyVbAI4HPV9U/ZqNOSZIkSUu+TgOQJPcCvg1sAQQo4HXAOsABwK5Jtqiqq7usV5JGWV51+lw3oW91yIZz3QRJ0ojpegjWh4CnAp8Djhh3/kvAW4FHAe/tuE5JkiRJI6LrAGQn4PtV9SrgD2Mnq+raqtofOBJ4zqCVJLlfkoOSXJLkuiSHJll+gPJ2TPLrJDcluTnJT5NsMGg7JUmSJN1Z13NA7gucPUX6ZcCqg1SQZDngh8BG7fFc4MXAA4Gn9VHebsAhwI3Aj4EVgE2Ak5I8pqouHKS9kiRJku7QdQ/IucATeyUkuTuwNXD+gHW8mSb42L+qtq2qNwLbAtsk2XkmBSVZEfgIcA7wyKraqqo2Aj4A3Bt404BtlSRJkjRO1wHIIcCTk3weeBBAkhWSPBH4OrAucPCAdbwSuB7Ya+xEVZ0GHA+8cIZlPZcm0HhBVf1p3PmPtUeHYUmSJEkd6nQIVlUdlOQxwG40K2AFuLJNDvC5qjqo3/KTrAqsCRxTVTdMSD4eeNsM2/u5JF+rqusnJN2jPd7aX0slSZIk9dL5PiBV9eokhwPPA9ZuT58PHFFVJw9Y/Crt8cweaRcAKyRZsaqu7JHeU4/gA2C79njqDNsnSZIkaQqzshFhG2gMGmz0MrbS1eU90q5pj6txR6/LjCVZimbJ4NtolhOezjVnTZK0Vr/tkCRJkuajTueAJFmpy/J6GBsS1Ws39Zvb4z0HrOMNwPrAZ6tq0AnzkiRJksbpugfk0iTHA18DjpqFHc/H5n3co0fa0lOkTUuSdYAPAn8G3jHd66pq/UnKOwtYr9/2SJIkSfNN16tg/TfNylGfB/6S5JgkL0py747Kv7Q9rtEjbeX2eHU/BSdZhiZwWhZ4cVVds5hLJEmSJM1QpwFIVb2CZqPBrWmCkA2BLwF/TXJEkp2SLDtA+X8HLqH38rgbt8e/9ln8p4DHAe+tqpP6LEOSJEnSFLruAaGqbquqH1XV66pqdWAz4NM0QcPX6D9AGPM9YFGSsRW2xnovdgDOqapeE9SnlORNNPuLfI9mCJYkSZKkWdB5ANLDee3jIpq9QAYdjnUgzR4jhydZve1ROZhm9asvz7SwJC8A9m/b+MKqqgHbJ0mSJGkSs7IMb5KH0uwy/lzgScBSwO+A99D0gvStqs5IsgfNbuUXA7cAywCnAfuNa8M+wFVV9dEp2nlvmuAlwBnA25JMrO/dg7RXkiRJ0h06DUCS7EkTdDyO5o/6c4H/Ar5eVWd3VU9VfSLJ6cDuNHNOjgP2raqbx2Xbk6bXZdIABHgMd+wt8rxJ8hiASJIkSR3pugdkb5o/+vejCTp+3XH5/1JVpwCnTJGeydLG5fkpTaAkSZIkaQi6DkA2qapTOy5TkiRJ0jzR9TK8Bh+SJEmSJjWMVbAkSZIkCTAAkSRJkjREBiCSJEmShqbvSehJNm9/PK2qbpxwbkpVdXK/9UqSJEkaXYOsgnUizY7kj6TZ72P8ucmkTV9qgHolSZIkjahBApBdaYKJy8ade/lgzZEkSZI0n/UdgFTVoT3OHTZQayRJkiTNa05ClyRJkjQ0BiCSJEmShsYARJIkSdLQGIBIkiRJGppOA5Akmyd56CRpSyU5Lcnnu6xTkiRJ0ujougfkBOAVvRKq6jbgt8D2HdcpSZIkaUR0HYBkMemXA/fsuE5JkiRJI2I25oD03Ak9ybLA5sB5s1CnJEmSpBEwyE7oACT544RTb0jykh5ZVwaWA148aJ2SJEmSRtPAAQhwMXf0eqwBXAtcNCHP7cBJwFeq6n87qFOSJEnSCBo4AKmqRWM/J7kd+O+q2mvQciVJkiTNP+4DIkmSJGlouhiCNd77aYZaSZIkSdJddBqAVNX7uyxPkiRJ0vzSdQ8IAEkCrAYsNSFpeWCHqtp7NuqVJEmStGTrNABJshxwAM1Su8tMkdUARJIkSVqAup6EvhfwSuAY4BfAbTTBxv7AP4CrgZd3XKckSZKkEdH1EKznAd+pqn9PsgFNEPKdqjo9yTHACcB9O65TkiRJ0ojougdkNeAPAFX1K+B6YJP2+Y+B44BXdVynJEmSpBHRdQByCfCocc9/DWw07vnZwMM6rlOSJEnSiOg6ADkC2DbJV9vnJwA7JNk0yZrAdsClHdcpSZIkaUR0HYB8kGaY1Y7t84OAW4GTgfOBdYDPdlynJEmSpBHR9UaEN9D0gDy2fX55ko2BtwOrAP9TVZ/qsk5JkiRJo2NWNiKsqjPG/Xw+sNts1CNJkiRptHQ9BEuSJEmSJmUAIkmSJGloDEAkSZIkDY0BiCRJkqSh6XsSepI/9nlpVdVa/dYrSZIkaXQNsgrWxUB11RBJkiRJ81/fAUhVLeqwHZIkSZIWAOeASJIkSRoaAxBJkiRJQ2MAIkmSJGloDEAkSZIkDc0gq2BNKskiYDNg1ap6XZKtgEcCn6+qf8xGnZIkSZKWfJ0GIEnuBXwb2AIIzTK9rwPWAQ4Adk2yRVVd3WW9kiRJkkZD10OwPgQ8FfgccMS4818C3go8Cnhvx3VKkiRJGhFdByA7Ad+vqlcBfxg7WVXXVtX+wJHAczquU5IkSdKI6DoAuS9w9hTplwGrdlynJEmSpBHRdQByLvDEXglJ7g5sDZzfcZ2SJEmSRkTXAcghwJOTfB54EECSFZI8Efg6sC5wcMd1SpIkSRoRna6CVVUHJXkMsBvNClgBrmyTA3yuqg7qsk5JkiRJo6PzfUCq6tVJDgeeB6zdnj4fOKKqTu66PkmSJEmjY1Y2ImwDDYMNSZIkSXcy8ByQJKsmeVaSnZJstJi8qyXZb9A6JUmSJI2mgQKQJB8BLgaOBr4G/DzJmUnWnZBvvSRfBP4IvGWQOiVJkiSNrr6HYCV5OU0wcT3wLeBy4LE0S+0em2QdYBPg7cDTaYKdPwD7D9hmSZIkSSNqkDkgrwZuAB5fVReMnUyyJ7A3TbCxBs3qVz8GPlJV3xmgPkmSJEkjbpAhWOsD3x4ffLTGltl9CHAE8MSqeorBhyRJkqRBekDuCVw08WRVXZUEYN+q2nOA8iVJkiTNM4OugnXbFGm3Dli2JEmSpHlm0H1AXpZkqx7nC3h5kq17pVXVpgPWK0mSJGkEDRqAPKh9zCStBqxTkiRJ6kRedfpcN6EvdciGc92Evg0SgKzZWStmKMn9gA8CzwGWp1kG+I1VdW2f5T0S2A/YFLgR+GRV/VdHzZ0zo/qGgtF+U80276skSRplfQcgVXWXCejDkGQ54IfARu3xXODFwAOBp/VR3iOAnwD3Ab5Cs2zw3kmYD0GIJEmStCQZdAjWXHgzTfCxf1XtAZDky8CpSXauqm/MsLxPASsCz6mqY9ryzgI+kOTLVXVxh22XJEmSFrRBV8GaC6+k2X19r7ETVXUacDzwwpkUlOQhNDu3nzAWfLQOAm4BnjtwayVJkiT9y0gFIElWpZl7cnxV3TAh+Xhg0QyL/Lf2eKdNEtuyT+2jPEmSJElTGKkABFilPZ7ZI+0CYIUkK3ZY3sNmUJYkSZKkxRi1OSDLt8fLe6Rd0x5XA67sqLzVplNIO2ekl3UvuOAC1l9//Wk2p2OX3Tg39XZg/Z8sO9dNWHJ5X+cf7+n85H2df7yn89OI3te5vKcXXHABwIP7vX7UApCx3dX/0SPt5vZ4zw7Lm0lZvdx+00033XD22Wf/34DlLInWao8XzEbhZ181G6VqMWb1noL3dY74Xp1/fK/OT75X55/5fE8fTO+/n6dl1AKQsXkf9+iRtvQUaf2WN62yqmqOujjmzlivz0L83ecr7+n85H2df7yn85P3df7xnk5u1OaAXNoe1+iRtnJ7vLrD8mZSliRJkqTFGKkApKr+DlwCbNAjeeP2+NcZFHlGe5ysvJmUJUmSJGkxRioAaX0PWJRk7bETSZYBdgDOqapeE8p7qqrzgPOAlyYZG8JFkscA6wEnd9ZqSZIkSSMZgBwIFHB4ktWTLAscTLNi1Zf7KO8AYG3g00mWS7I68AUgwFc6arMkSZIkRm8SOlV1RpI9gI8BF9PsWL4McBqw31i+JPsAV1XVRxdT5EHA5sArgJcAS7WPj1fVT7r/DSRJkqSFK1U1123oS5JNgd2BVYHjgH2r6uZx6QVcVFVrTLO8XYAXtU8PraqvdttiSZIkSSMbgEiSJEkaPaM4B0SSJEnSiDIAkSRJkjQ0BiCSJEmShsYARJIkSdLQGIBIkiRJGhoDEEmSJElDYwCyACT5U5Ka8Fg0x216QpJXzmUbRtkSek/XTPL2uWzDqPO+zj9L6D3183dAS+h99b06gCX0ni6b5MNJlp3LdsyGkdsJXX37MXDyuOcXzVVDWhsBn0ryl6r67hy3ZVQtafd0PeBDSa6vqoPmuC2jzPs6/yxp99TP324saffV9+rglrR7ujKwK7Am8Pw5bku3qsrHPH8AfwLeN0X6dsBpwA3A2cCzOqr3XsCZk9UNfBS4GnjQXL9Go/ZY3D1t8ywFHAcc2kF9927v18XAbcA1wMHAPSfkewNwM7DBXL9Go/iY6r4CSwN7AucDt7bv168BKw1Y58rAm4EvAJ8Cnt4jj/d1Fu7pdF//AetfB7gReNmE837+zuJ9nZD3AcDfp5t/kjLeDNQkjzXG5fO9Ogv3FNhhitd/UQd1j/0bORdYZkLaE4F/AK+f69eoy4c9IAtckp2Ar9P8w/8czTcoRyXZoqp+PEC5AT4PPBo4cpJsbwMW0fynu32/dWlS/wVsDRw2SCFJlgK+B2wGXAJ8H9gQeBXwIOCZY3mr6pNJngp8LsmG1X56qhOHAS8ArgB+CDyS5huxdZJsVFW3zrTAJOsD/wOsBPwReDDwuiSfrqrXjuXzvs6O6b7+AzoEWKbHeT9/h+ejwP0HLGNDmv+nD+mRdvXYD75XZ82GwC3Avj3Suugl2Z/m38gLq+qm8QlVdWqSdwIfTnJUVV3SQX1zzgBkAUuyPHAQcBWwUVVd1J7/Ls232+v3We4ywBdZTHdhVd2e5LXAT9s/oH7RT326szb42xd4a0dF7koTfHwCeGtV3ZLkPsDPgGckeXxV/Xpc/jcBvweeB3yzozYsaEm2pgk+jgBeXlXXJ1maJhjcEngWcNQMy7wb8BXgJuARVXVhknsDRwOvSfLFCe/JN+F97Uwfr38/dbwceEqvND9/hyPJFsAuHRS1IXBqVb17GnnfhO/Vrm0InDvN139GkmwFvAQ4oqqOmyTbgcArgHcBr+u6DXPBSegL2/Y03X4HjAUfrf8HrJfkcX2WeziwI9P4A7iqTgV+QvNturrxEWAPYPeOytsF+E1V7V5VtwBU1XU0PWYAG4zP3P5b+ibe0y7tAvwFeElVXQ/Q3otPtekbTHbhFJ4JPJamW//Ctszrgf3a9CePz+x97dyMXv+ZSvKAtqz/myyPn7+zq/0y7mCmuAfTLOdewCOA30wnv+/VWbEB03z9Z6KdXH4wzZDaN0+Wr+3h/hjwkiTLdd2OuWAAsrD9W3v8zoTzP6MZM7xogLKfAnxrmnm/CTxjgLp0Z/ehmcfziS4Kq6rN6P3H0D3aY6+hP98ENm+/0dWAquolwMOr6sYJSVPdg8X5MbAFzfC68ZZujzf0uMb72p1+Xv+Z+CiwAvDGxeTz83f2vIsmcHjNgOU8nubvtZn0Uvle7UiSB9HM1ZqNXsK9gLWA91fVnxeT92hgWWDzWWjH0BmALGyrALcDZ40/2UbaFwEP67Pcnavq5zPIfwqwapJV+6xPd/aaqjq2ywKrqtcfQ9u1x1N7pJ1C84fUo7psx0I21vMxwVT3YHHlXVVVJ44fI97O93kjzVjn/+1xmfe1I32+/tPSzgHYhaY39DeLye7n7yxIsi7wDuBrHXweb9ged01ySZJ/JjkjyWvboXy9+F7tztjr//QkFya5MckfkuyZ5B5TXjmFJOvRjBS5AVguyceSvDHJ/Xrlr6qraIbWPb7fOpckBiAL2/LANVV1c4+0a4DV+im0qm6b4SWXtcfV+6lPd9bH6z9jSZ5E08t1clWd06MNV9GMbfeezpIkDwX+HbiQZiLzIGWtluQNNN/wbUkzLOiPE/N5X2fHdF//aZY1NuznPOD907jEz9/ZcTBwPd0MhX1Ce3wicCzwWZr/vw+k6em6C9+rnRp7/Z8EnAR8mmalsX1oFvHp1740QeK9aN6rbwIOAH6bZKNJrrmMeXJPnYS+sN1Ks7RbLzcD9xxSO65sj/NiXON8135LeyDN8oPvmCLrlXhPZ9MBNKsbvbODoPPJbXkBfsfU3757X7s3k9d/cfYE1ga2qKobmzUppuTnb8eS7ErzBc1Lq+pvHRR5Dc0CFC8b641O8g7gROANST4xNo9oAt+r3biZZuXBl1bVX+Ff/w8eCWyfZLOZrhqa5PHcMfRxX5qg8m7Ai2mG7h2ZZJ2qmvg32ry5p/aALGw3cMcY8omWniKtayu1x0HHPGs49qSZkHdgVf2sV4Z2Ja4H4D2dFUl2AZ4DfLeqBvkGDoCq+gbN/XoD8BDgx72G5HhfZ8d0X//FaYf9/Cfwuao6aZqX+fnboSQr0fxBeVxV/XcXZVbV66tqp/FDYds/TPem2e/pmROv8b3anarau6q2HQs+2nO3AWMrYm3fR7HPbo/fqKr/rKrzq+rcqtoL+CTNEvfb9rhuJebJPTUAWdguBVZK0qunY2XGrS0+y8aGel08pPrUpySbAu+h+Zb27VNkfQBNEOs97ViStWlWv7qMZonkTlTVlVX1KeD1NO/J3Xpk877Okmm+/pNq/+A8hGaviLfN4FI/f7u1P81E4WGsQHVue1yzR5rv1dk31eu/OGPDqHoFqT9qjw/vkbYa8+SeOgRrYTujPW5AsxQj8K/lG9ek2WNgGLYALqqqK4ZUn/qQZBWa8a430iw08M8psm/R5jtrijyaofbLgiNohkc+u6ouH7C89NiobOx9/4gel3hfO9TH6z+VZ9KsjnMi8LZxQ69WaI/PbYPXb03Yt8fP344keTTNEJrfAK/sMfztqUnuDhxfVcfPoNyVgH/2WIhixfa4bI/LfK92pN0zbeke75GpXv/Fua49XtAjbexv8zvNz217RdcFTu+jviWOAcjC9n2aVbBeybgAhGbDM4CTh9SOnYBjhlSX+tCuO34Uzbc2/15Vv1/MJTsB/9Nj2Vj1qV3t5ks0e0f8Z1WdOGB5HwBenWTNCaucje3Y3Kub3/vakT5f/6mMXbeI3kuoP6t9nA+MD0D8/O3O2D14XPuYaLP2cSswrQAkyaNovix8Pc3k5/HGVsE7u8elvlc7kOS+wN9o9uD4zwnJU73+i/Pb9vhgYOJCLo9ujxODxx1pNo7+aR/1LXEcgrWAtWtOHwnskmS3NDYF3gtcwRB6QJJsS9MDc/Bs16X+tJPtvglsAuy3uDkH7beA2wMHzX7rFpQDgefSjBnet4PyTqMZT/yJCUt5jg2t++H4zN7Xzs3o9V+cqjq0qjLxwR3DQ17enjt07Bo/f7vVLqt8l3vQ3gdo9npIVb1vBsX+HrgEeOf4eUFJnkyzcd2NNL2ijEvzvdqRqrqaZnW61yRZZ+x8kkfSrIJVwFf6KPpYmi8Z9mw3Ixwrdw2a1bD+yrgvgdsvAfegmd/Va+XSkWMPiF4LrE8zdvjjNKsr3ErzLfe1Y5naFRt2ZIZdx1Np31CfAg6rqn6+QdAAZnBPX0SzWsc/gduS7DMh/VdVdWRbZmj+0/txVf1gFpq9ICXZHHg1TY/lJT3uwYVV9fk275rAKxh3X3qpqu8mOZJmHsnjk5xM07uyiGbDq6PG1e997dgMX38/f+eh6dzXqrotyduArwLnJPkFzbKtT6IdwVBVfxlXpu/V7v0nzbyMXyf5Oc2X95vS/A291/ghjTP4/L08yVtp7tVvk/yQZmnl7Wk2E37BhN6rd7fnu/jyaYlgALLAtW+CDWkmLW4JXA58uKp+OSHrY2lWP5p21/FU2m/8vkKz0tYeg5anvkz3no7tgr4cvZfdPYymJw2aIPYxzJONkpYgY/fgbjTfek50EvD59ueH0tzX8fdlMi+kef+9FPgPmuE5rwMOmTA34eN4X2fDdF9/P3/np2nd16r6epJrgPfRfBZcRxOg7ltVEzch/Ti+VztVVT9JshnwIZq9WG6mCUg+VlUTeyqn/flbVQcnuQB4C7Azzdy+c4B9qurosXxJdqT5v/f5VXVlr7JGUe46/03zTZI/0ayaMH5Ox+cnWTd8qnKOo3nDDTw0K8leNEMNnlJV82JC1TAtofd0F+ALwHOq453YF4oO7+tngF9X1cQx4/20yfs6gCX0vern74CW0Pvqe3UAS+jn7yOAM2n+jbxz0PKWJAYgC0D7pnrohNNbzGQSa5IVaSaqr99j1ZZ+2rQq8PCa4eY9aiyh9/TewGZd/Ee6UHV0X5eiGTf++AmTm/ttk/d1AEvoe9XP3wEtoffV9+oAlsTP37bMnarqm12UtSQxANG0JHk4sEZV/c9ct0Xd8J7OT+2SnYvm439YC5Xv1fnJ+zr/+Pk7fQYgkiRJkobGZXglSZIkDY0BiCRJkqShMQCRJEmSNDQGIJIkSZKGxgBEkiRJ0tAYgEiSJEkaGgMQSZIkSUNjACJJWqIkuX+SvZPMy/+jkrw9yZpz3Q5JmituRChJWmIkuQdwAvAAYOOqumaOm9SpJHcHTgRWBjaab7+fJE3HvPx2SZI0c0l+neT2JKvMYTM+DKwNPH0+/nFeVbcCzwEK+PwcN0eS5oQBiCSJJCsDjwUCbD1HbdgI2B3Yrar+OBdtGIaqugJ4AbBDku3nuDmSNHQGIJIkgK1ogg+YowAE2Af4QVV9e47qH5qq+hVwMM3vLEkLigGIJAlgG+Ba4CjmIABpJ2VvDew37Lrn0EeA9ZM8ea4bIknDZAAiSYKmB+QXwE+BByZ59JDr3xb4G3DykOudM1V1IXA68Iy5boskDZMBiCQtcEnWB1YHft4+oEcvSJLtk5yZ5LokP07y9CRHJbkxyaJx+VZJ8pkklyW5qb3mBYtpxuOBn1fV7RPqfGySSvKZHu35QJu2zYTzOyc5Lck/k1yR5LBeE+vbso9J8tck17bXPLtHvpe19SxKcp8k+yT5Q5Kv9ci7UZIfJPlbW+bPk0wVYJzS/u6StGAYgEiSxv6A/znNN/K3jDsHQJItaIZn3Qx8AXgY8D3gauAg4PI23yrAqcBLgB8BnwbuBRye5GVTtGE14LKJJ6vqDOD3NBO27z4heUfgr209Y+18B/B1YJm27hOAFwEnJlluXL51aHpbFgHHtL/TCsDRSTafpI0rtr/bW9p6zx+fmOThbX2bAEcDnwXuB3w7ySaTlHkZTfAnSQuGAYgkaay34+dV9U/gTGDzJMuMy/MW4Apgs6raHdihPf/HqnpLVZ3VPv8Q8FBgu6p6cVW9CdiQZnjV+6dow3LAVZOkHU6zL8hTx04kWRdYD/haVd3WnluTZlL3j4AN23Y9D9gLWJdm5akxr6IJjF5YVf/RtnMT4CZgl0na8VGawOOhVbV5Vb17Qvoz2jJfU1W7VdUewObAn4GnTFLmle3vLkkLxsRvkyRJC0i78d9TgAuq6u/t6Z/TBA1P5o7ehbWB89oABZogBcZ9e9/uXP5cmj+qt0yy5biqrgYekeShVXVRj6b8gybI6OVwYG9gZ+C49tzz2uNXxuV7LrAUcB3wvmRsUS/Ghl9tRtPTQVW9hSaoGmv7vWl6fe4GTLZL+TLADlV19STpf2uPj0/ytaq6var+CqwxSX6AlYAbpkiXpHnHAESSFrZNgXsCayWpCWnbcEcAchHwhCTLVtWNwNgk9fPG5V+JZhgTwJ6T1PfAtqyJLqbpObmLqvpjklNphmG9ut3Mb0eagOgX47Ku3R63bx+96v6XdqjVi2gCk0fQBB9FE8T08uEpgg+AI4A3A28FXtq2+afA4ZMEXdAMPbt4ijIlad5xCJYkLWxjcz2+CnywfXyI5g/x8RPRPwjcHzg5ycdp5oNcARzWo8zvVFUmefy8R35o5p5s2vbI9PJVmjkYWyV5GPA47tz7Md6Gk9S97ViGJG8FTqLp/fkB8FLgQcD/TVIm3NHr01NV3UwzjOs5wKHAvYEPAOeMn6Q/wRY0v7skLRj2gEjSwjYWgLylqv4ydjLJdsDjkqwE/B34D+A0mjkOrwB+Cbxx3LAtaCaiXwusM7GSJE8CngAcXVV/7tGOHwCfoVkO+Hs90r9OMwdjJ+Cc9tzEAGRsUvg6wK/G1X034DXAZVV1ZJKlaAKDc4FHV9Ut4/It26PuaUnyCGDZqjqGZmI7SR5Hs7zxe4ETJ+RfH3jkWF5JWijsAZGkBSrJA2iWgP39+OCjdQLNzuhb0cwHeQnNEKRHVdV9qmqLqvrt+AvaJXSPpJnrsdO4epYGDgQ+TjPX4y6q6lKalaP2zLjJG+PS/9K2aXuayeSnVdX5E7IdBdwGvKmd0zFme+BTNEOtoBkmthzwt3HBx1I0Ac5dluudgX2A09vXdcx5wI00w9Mmeg9warsruiQtGPaASNLCtRVNkHFCj7QTgDfR9JAcR7NC1SFJnknT03EDzYpQJ1XVH8Zd905gS+DrSV5C0yvxVOCxwHur6sop2vNu4AzglTRL2E50eNvmFdu23Uk7V+S9NIHA75IcSzMMamfgEmDfNt+VSX4FPDnJt2hWqdqWZrL4P4Hlp2jjVL5AMzn+V0m+25b1tLYNR47PmGTrtl1b9FmXJI0se0AkaeEam+PRKwA5maY3YeuquoJmT4370AQXu9MMYToE+H2SV4xd1PZUPJHmj/GNaZa7vQ3Ypao+MFVjquocmiDkgEn2zTiSZpnc24C7bALYlvFB4IU0QdKuNAHLN4BNqmr8PiM7AN+iCQB2pZkY/xSaFcAelWRtZqiqfkATsJ1DsyLXa9qkNwHvG8vXDtU6HDiwqk6aaT2SNOpSNXHRE0mS7pDkNcDHgDXH/ohv50usBBwL3L2qHtdhfV+jCY7Wrqqrxp0P8CeaIWPbTnL5Eq3dTPFsmg0It24nrkvSguIQLEnS4qxCswfGkUl+SbPPxjI0E6gfT7PiU5deAjxjLPhoh309nWYZ3YfQ9MCMpKq6NckbgJ8ZfEhaqOwBkSRNqV0a9x00w5bWolkJ62rg98A3gU/P5h/TSZ5NswrWP4FPVNX7ZqsuSdLsMwCRJEmSNDROQpckSZI0NAYgkiRJkobGAESSJEnS0BiASJIkSRoaAxBJkiRJQ2MAIkmSJGloDEAkSZIkDY0BiCRJkqShMQCRJEmSNDQGIJIkSZKGxgBEkiRJ0tAYgEiSJEkaGgMQSZIkSUPz/wGI8wogVINOTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'A2':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'A2':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of A2(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4,5,6]# 斯皮尔曼相关系数\n",
    "x2 = [2,1,4,5,6,3]\n",
    "d = [0,0,0,0,0,0]\n",
    "dd = [0,0,0,0,0,0]\n",
    "for i in range(0,6,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,6,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-6*sum_dd/(6*(6*6-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "618b8068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23, 54, 20, 218, 855, 199]\n",
      "[3, 86, 225, 257, 748, 12709]\n",
      "14028\n",
      "[0.16395779868833762, 0.3849443969204448, 0.142571998859424, 1.5540347875677218, 6.094952951240376, 1.4185913886512689]\n",
      "0.6571428571428571\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'A3':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'A3':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of A3(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4,5,6]# 斯皮尔曼相关系数\n",
    "x2 = [2,3,1,5,6,4]\n",
    "d = [0,0,0,0,0,0]\n",
    "dd = [0,0,0,0,0,0]\n",
    "for i in range(0,6,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,6,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-6*sum_dd/(6*(6*6-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "15efe0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[36, 100, 237, 87, 0, 0]\n",
      "[184, 354, 9948, 25184, 52, 0]\n",
      "35722\n",
      "[0.10077823190190918, 0.27993953306085884, 0.6634566933542355, 0.2435473937629472, 0.0, 0.0]\n",
      "0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'A4':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'A4':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of A4(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4]# 斯皮尔曼相关系数\n",
    "x2 = [1,3,4,2]\n",
    "d = [0,0,0,0]\n",
    "dd = [0,0,0,0]\n",
    "for i in range(0,4,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,4,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-4*sum_dd/(4*(4*4-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9ad3433e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15, 97, 110, 38, 0, 0]\n",
      "[99, 170, 5341, 22017, 236, 0]\n",
      "27863\n",
      "[0.053834834727057385, 0.3481319312349711, 0.3947887879984209, 0.13638158130854539, 0.0, 0.0]\n",
      "0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'A5':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'A5':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of A5(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4]# 斯皮尔曼相关系数\n",
    "x2 = [1,3,4,2]\n",
    "d = [0,0,0,0]\n",
    "dd = [0,0,0,0]\n",
    "for i in range(0,4,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,4,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-4*sum_dd/(4*(4*4-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bfe9a19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 28, 168, 28, 0]\n",
      "[0, 1, 22, 244, 11199, 1358]\n",
      "12824\n",
      "[0.0, 0.007797878976918278, 0.21834061135371177, 1.3100436681222707, 0.21834061135371177, 0.0]\n",
      "0.9166666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'A6':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'A6':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of A6(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4,5]# 斯皮尔曼相关系数\n",
    "x2 = [1,2,3,5,4]\n",
    "d = [0,0,0,0,0]\n",
    "dd = [0,0,0,0,0]\n",
    "for i in range(0,5,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,5,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-5*sum_dd/(5*(5*5-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6688b0b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 32, 161, 164, 25, 0]\n",
      "[98, 157, 2480, 53450, 32801, 81]\n",
      "89067\n",
      "[0.006736501734649196, 0.03592800925146238, 0.1807627965464201, 0.1841310474137447, 0.028068757227704987, 0.0]\n",
      "0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'B1':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'B1':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of B1(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4,5]# 斯皮尔曼相关系数\n",
    "x2 = [1,3,4,5,2]\n",
    "d = [0,0,0,0,0]\n",
    "dd = [0,0,0,0,0]\n",
    "for i in range(0,5,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,5,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-5*sum_dd/(5*(5*5-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0efdbbfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[26, 143, 334, 100, 1, 0]\n",
      "[389, 711, 2795, 38700, 848, 0]\n",
      "43443\n",
      "[0.05984853716363972, 0.3291669544000184, 0.7688235158713717, 0.23018668139861428, 0.002301866813986143, 0.0]\n",
      "0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'B2':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'B2':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of B2(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3,4]# 斯皮尔曼相关系数\n",
    "x2 = [1,3,4,2]\n",
    "d = [0,0,0,0]\n",
    "dd = [0,0,0,0]\n",
    "for i in range(0,4,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,4,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-4*sum_dd/(4*(4*4-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3180cd4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[269, 1084, 454, 0, 0, 0]\n",
      "[2764, 8001, 27798, 0, 0, 0]\n",
      "38563\n",
      "[0.6975598371495993, 2.810984622565672, 1.1772942976428182, 0.0, 0.0, 0.0]\n",
      "0.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'B3':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'B3':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of B3(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3]# 斯皮尔曼相关系数\n",
    "x2 = [1,3,2]\n",
    "d = [0,0,0]\n",
    "dd = [0,0,0]\n",
    "for i in range(0,3,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,3,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-3*sum_dd/(3*(3*3-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "24ee50e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1901, 5652, 2952, 0, 0, 0]\n",
      "[7713, 60672, 99494, 0, 0, 0]\n",
      "167879\n",
      "[1.132363190154814, 3.366710547477648, 1.7584093305297268, 0.0, 0.0, 0.0]\n",
      "0.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'C1':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'C1':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of C1(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3]# 斯皮尔曼相关系数\n",
    "x2 = [1,3,2]\n",
    "d = [0,0,0]\n",
    "dd = [0,0,0]\n",
    "for i in range(0,3,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,3,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-3*sum_dd/(3*(3*3-1))\n",
    "print(spearman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a635fabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[889, 237, 5, 0, 0, 0]\n",
      "[1545, 16830, 1401, 0, 0, 0]\n",
      "19776\n",
      "[4.495347896440129, 1.1984223300970873, 0.025283171521035597, 0.0, 0.0, 0.0]\n",
      "0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6,1):\n",
    "    failure_count_of_model[i] = 0\n",
    "    all_count_of_model[i] = 0\n",
    "    failure_percen_list[i] = 0.0\n",
    "\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    if data1[i][0] != 'C2':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count_of_model[y1]=failure_count_of_model[y1]+1\n",
    "print(failure_count_of_model)\n",
    "\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    if data2[j][2] != 'C2':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count_of_model[y2]=all_count_of_model[y2]+1\n",
    "print(all_count_of_model)\n",
    "\n",
    "\n",
    "all_failure = 0\n",
    "for i in range(0,6,1):\n",
    "    all_failure = all_failure + all_count_of_model[i]\n",
    "print(all_failure)\n",
    "for i in range(0,6,1):\n",
    "    failure_percen_list[i] = (failure_count_of_model[i]/all_failure)*100\n",
    "print(failure_percen_list)\n",
    "\n",
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>100 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate of C2(%)\")  # 添加y轴标签\n",
    "\n",
    "x1 = [1,2,3]# 斯皮尔曼相关系数\n",
    "x2 = [3,2,1]\n",
    "d = [0,0,0]\n",
    "dd = [0,0,0]\n",
    "for i in range(0,3,1):\n",
    "    d[i] = x1[i]-x2[i]\n",
    "    dd[i] = d[i]*d[i]\n",
    "sum_dd = 0\n",
    "for i in range(0,3,1):\n",
    "    sum_dd = sum_dd + dd[i]\n",
    "spearman = 1-3*sum_dd/(3*(3*3-1))\n",
    "print(spearman)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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